Recently a new methodology based on local density of state (LDOS) calculations using topological and semiempirical methods was proposed to identify the carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs). In this work we perform a comparative study of this methodology with principal component analysis (PCA) and neural networks (NN). The PCA and NN results show that LDOS quantum chemical descriptors are relevant descriptors to identify the carcinogenic activity of methylated and non-methylated PAHs. Also, we show that the combination of these distinct methodologies can be an efficient and powerful tool in the structure-activity studies of PAHs compounds. We have studied 81 methylated and non-methylated PAHs, and our study shows that with the use of these methods it is possible to correctly predict the carcinogenic activity of PAHs with accuracy higher than 80%.

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Recently a new methodology, called electronic indices methodology (EIM), based on local density of state calculations (LDOS) using topological and semiempirical methods, was proposed to identify the biological activity of polycyclic aromatic hydrocarbons (PAHs). In this work we apply the concepts of the EIM approach to classify the progestational activity of 21 17alpha-acetoxyprogesterones (steroid hormones) (APs). The EIM approach pointed to a few descriptors, which correctly classify the active/inactive compounds of this class (approximately 90%). We show that these descriptors arise naturally from principal component analysis (PCA) and neural network (NN) calculations. Moreover, using only the parameters from EIM, instead of a large set of descriptors that have been used before to describe the biological activity of these hormones, we slightly improve and simplify PCA and NN results. Finally, the molecular region related to the chemical activity of these hormones naturally appears in our theoretical analysis, from the local density of states of the frontier orbitals. This shows the generality of the principles of EIM approach, and confirms that the combination of these distinct methodologies can be an efficient and powerful tool in the structure-activity studies of many different classes of compounds.

Polycyclic Aromatic Hydrocarbons (PAHs) constitute an important family of molecules capable of inducing chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying the PAHs' carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors that can be directly correlated with some models proposed to PAHs' metabolic activation mechanism leading to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic activity. This descriptor has been only recently discussed in the literature as one new possible universal parameter for defining the biological activity of several classes of compounds.

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